Adiposity and Binge Eating Are Related to Liking and Wanting for Food in Brazil: a Cultural Adaptation of the Leeds Food Preference Questionnaire

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Article: de Carvalho-Ferreira, JP, Finlayson, G orcid.org/0000-0002-5620-2256, da Cunha, DT et al. (3 more authors) (2019) Adiposity and binge eating are related to liking and wanting for food in Brazil: A cultural adaptation of the Leeds Food Preference Questionnaire. Appetite, 133. pp. 174-183. ISSN 0195-6663 https://doi.org/10.1016/j.appet.2018.10.034

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[email protected] https://eprints.whiterose.ac.uk/ 1 ADIPOSITY AND BINGE EATING ARE RELATED TO LIKING AND WANTING FOR 2 FOOD IN BRAZIL: A CULTURAL ADAPTATION OF THE LEEDS FOOD PREFERENCE 3 QUESTIONNAIRE

4 Joana Pereira de Carvalho-Ferreira1,2, Graham Finlayson3, Diogo Thimoteo da Cunha4, Gabriele 5 Caldas2, Daniel Bandoni5, Veridiana Vera de Rosso1

6 1Department of Biosciences, Federal University of São Paulo, Santos-SP, Brazil 7 2Post Graduation Program on Foods, Nutrition and Health, Federal University of São Paulo, 8 Santos-SP, Brazil 9 3Institute of Psychological Sciences, University of Leeds, Leeds, West Yorkshire, United Kingdom 10 4 School of Applied Sciences, Campinas State University, Limeira-SP, Brazil 11 5Department of Health, Clinic and Institutions, Federal University of São Paulo, Santos-SP, Brazil 12

13 Declarations of interest: none.

14 Corresponding authors: Joana Pereira de Carvalho-Ferreira and Veridiana Vera de Rosso, 15 Department of Biosciences, Federal University of São Paulo, Rua Silva Jardim 136, Santos, SP CEP 16 11015-020, Brazil. Email: [email protected] (Carvalho-Ferreira JP) and 17 [email protected] (de Rosso VV). 18

26 27 ABSTRACT

28 The Leeds Food Preference Questionnaire (LFPQ) measures separable psychological

29 components of food reward (Liking and Wanting). In this study a cultural adaptation of the LFPQ

30 for a Brazilian population (LFPQ-BR) was examined by comparing liking and wanting scores in

31 fasted and fed states and their association with adiposity and disturbed eating. A culturally

32 adapted food picture database was validated by an online questionnaire completed by 162

33 individuals. Cluster analysis verified if the foods were accurately perceived in terms of

34 sweetness, fat and calorie content. Subsequently, 48 male (N=21) and female (N=27) adults with

35 mean Body Mass Index 26.6 (0.9) kg/m2, and mean age 32.8 (1.4) years, were evaluated by the

36 LFPQ-BR before and after a fixed test meal. The Binge Eating Scale was used to measure binge

37 eating symptoms. There was a decrease in explicit liking, implicit wanting, and explicit wanting

38 scores for food in general in the fed condition. The implicit and explicit wanting and explicit liking

39 scores for high-and-low fat savoury food decreased and for high-and-low fat sweet foods

40 increased to a greater extent after the savoury test meal. Body Mass Index was found to predict

41 implicit wanting for high fat relative to low fat foods. Binge eating symptoms predicted high fat

42 sweet explicit liking and explicit wanting in the fed condition. Finally, high fat sweet preference

43 was found to be sex-related as females had greater implicit wanting for high fat sweet foods in

44 fasted and fed states. The results presented here indicate that the LFPQ-BR is a useful

45 instrument for the evaluation of liking and wanting for food in Brazil.

46 Keywords: food reward; liking; wanting; binge eating; adiposity; Brazil.

49 50 INTRODUCTION

51 Overeating is an important risk factor for weight gain and because obesity prevalence is

52 increasing worldwide (Swinburn et al., 2011), acquiring a comprehensive view of the factors that

53 lead to overeating is crucial. It is already known that eating behaviour is not only modulated by

54 hypothalamic circuitry, but is also determined by the hedonic system responding to an

55 obesogenic environment (Berthoud, 2006). Natural rewards, like food, stimulate activation in

56 the mesolimbic dopaminergic pathway (Lutter and Nestler, 2009) and individual variability in

57 sensitivity to reward is a psychobiological trait linked to the development of obesity (Beaver,

58 2006).

59 Therefore, overeating is thought to be more than an imbalance of hormonal satiation

60 and satiety signalling, but also occurs due to an excessive or weakened response to the hedonic

61 aspects of food (Blundell and Finlayson, 2004). Considering the role of cognitive and hedonic

62 aspects of eating behaviour helps to understand more than meal size and frequency, but also

63 food preferences and choice, which are partly driven by the motivation and experience of

64 pleasure obtained from food (wanting and liking for food). This distinction is key to comprehend

65 overeating leading to weight gain in an environment where highly palatable and energy-dense

66 foods are plentiful and affordable (Dalton et al., 2013b). Importantly, it is suggested that high

67 palatable foods are consumed frequently even when energy needs are satisfied, while less tasty

68 foods are not overconsumed and this is one of the key risk factors associated with obesity

69 (Kenny, 2011). Thus, palatability is a key contributor to the decision to eat particularly for

70 individuals susceptible to reward-driven eating.

71 The Leeds Food Preference Questionnaire (LFPQ) developed by Finlayson et al. (2007)

72 is a computer-based platform designed to measure the separate constructs of liking and wanting

73 according to key dimensions of food (e.g. fat/protein content and taste). The questionnaire

74 cales and includes an 75 s of decisions between pairs of

76 foods. Previous research with the LFPQ has demonstrated that liking for food, i.e. the perceived

77 or expected pleasure value of the food, the appreciation of its sensory proprieties, or a judgment

78 of the degree of pleasure it elicits (Dalton and Finlayson, 2014) is greater in fasted than fed states

79 (Finlayson et al., 2008) and that liking for a recently eaten food decreases in a manner consistent

80 with sensory specific satiation (Griffioen-Roose et al., 2010). Wanting on the other hand, i.e. the

81 attraction that is triggered by the perception of a food or a food related cue in the environment

82 (Dalton and Finlayson, 2014) is also increased for food in general in a fasted state (Alkahtni et

83 al., 2016; Finlayson et al., 2008). However, it appears that wanting is more variable than liking

84 and may differ moment to moment depending on a number of factors such as hunger state,

85 time of day and/or the amount of attentional resources available (Dalton and Finlayson, 2014).

86 For instance, disordered eating patterns (Dalton et al., 2013a; Finlayson et al., 2012) and a state

87 of macronutrient imbalance (Griffioen-Roose et al., 2012) have been linked to increased wanting

88 for specific foods.

89 Because cultural issues play a major role in food choice, selection, and consumption,

90 cultural adaptation of LFQP may be necessary for use in Brazil . Studies have already shown that

91 food choices and their motivators have a strong ethnic and cultural relationship (Januszewska

92 et al., 2011; Prescott et al., 2002). As an example, a traditional Brazilian dietary pattern is defined

93 by the consumption of rice, beans, green vegetables, potato, lettuce, eggs, milk, and meat

94 (Marchioni et al., 2011). In contrast, people in the United Kingdom are likely to eat white bread,

95 butter, tea and sugar, cakes, puddings, ham, bacon, potatoes, and vegetables (Pryer, 2001).

96 Importantly, a previous research using the Leeds Food Preference Questionnaire (Leenaars et

97 al., 2016) indicated as a limitation of their study the imperfect suitability of using the translated

98 LFPQ for a Dutch population. They mentioned that some foods in the food database used in the

99 LFPQ would be less familiar to Dutch consumers in comparison with those from the UK. For

100 example, participants had difficult to identify one food as savoury or sweet. Therefore, 101 performing a validation study for the Brazilian Portuguese version (translation and food

102 database suitability) is of major importance in using the instrument in Brazil.

103 Brazil is currently facing an epidemic of obesity and overweight (Malta et al., 2014).

104 Recently, a food guide for the Brazilian population has been published suggesting that people

105 should consume less processed foods, i.e. foods high in fat, salt and sugar, indicating the harm

106 excessive consumption of these foods may bring about (Ministry of Health of Brazil, 2014).

107 However, the food choice goes beyond the perceived risk and benefits. There are several

108 benefits of having instruments to test components of food reward in a population. A reliable

109 method that allows the quantification of liking and wanting for different dimensions of food may

110 be valuable to link specific food preferences to health problems (such as weight gain and eating

111 disease), or identify risk factors (Dalton et al., 2013a; Finlayson et al., 2012). Moreover, it is

112 useful to test different types of diets under different contexts (Cameron et al., 2014; Griffioen-

113 Roose et al., 2012, 2011; Hopkins et al., 2016) and to link food reward to other circumstances,

114 such as sleep restriction (Leenaars et al., 2016).

115 The current study aimed to adapt the original LFPQ for the Brazilian population (LFPQ-

116 BR). Furthermore, we tested the sensitivity of this cultural adaptation by comparing liking and

117 wanting scores in fed and fasted states and their association with adiposity and scores on the

118 Binge Eating Scale.

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120

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122

123 124 MATERIAL AND METHODS

125 To meet the aims of the present study, some steps were performed. First, all the text from

126 the task was translated into Brazilian Portuguese and a food picture database was created and

127 validated for the Brazilian population. Following this cultural adaptation, an experimental

128 procedure was conducted to test the sensitivity of this new version of the LFPQ for a Brazilian

129 population.

130 a) Food database validation 131

132 To create a food database for the LFPQ-BR, ready-to-eat popular foods in Brazil were

133 photographed according to standardised procedure and 60 images went through an online

134 validation process. An online questionnaire was created and released by email and social media

135 and 162 respondents completed it. The questionnaire aimed to verify if the food pictures were

136 accurately recognized, habitually consumed, and that they were correctly perceived in terms of

137 fat content, number of calories and sweetness (high/low). The answers of any registered

138 dietician (i.e. nutritionist in Brazil) or nutrition specialists were excluded.

139 Each food picture was presented one at a time and the respondents were asked the

140 following questions for each one: do you recognize this food? (yes; I am not sure; no); have you

141 already eaten this food? (yes; I am not sure; no); do you like this food? (yes; more or less; no).

142 Additionally, participants were asked to rate on a 7-point Likert scale (with anchor points at

143

144 contain? How sweet is this food? How calorie-dense is this food?

145 Foods were considered adequate if: 80% of the sample recognize, habitually consume them,

146 and like them and if their mean values on the 7-point Likert scale answers were above 5 for the

147 high content of fat, calorie and sweet taste; and lower than 3 for the low content of fat, calorie

148 and sweet taste. Further, hierarchical W 149 distinctions among the food items. Three clusters were constructed: 1) content of fat; 2) content

150 of sweetness and 3) calorie amount, using the answers for each food on the Likert scale.

151 Dissimilarity dendograms, analysis of intraclass variance and centroid mean distance were

152 constructed.

153

154 b) The Leeds Food Preference Questionnaire (LFPQ) and Brazilian Portuguese Version of

155 the LFPQ (LFPQ-BR)

156 The LFPQ is a computer-based task developed to provide measures of different components

157 of food preference and food reward. The measures taken by the task and the methodology used

158 are summarized in Figure 01. Participants were presented with an array of 16 ready-to-eat food

159 images, which are common in the diet. Food images were chosen from a validated database,

160 four from each category, as follows: high fat sweet (HFSW), high fat savoury (HFSA), low fat

161 sweet (LFSW) and low fat savoury (LFSA) (Dalton and Finlayson, 2014; Finlayson et al., 2008).

162

163 Figure 1 Measures taken by the Leeds Food Preference Questionnaire. 164 Text from the original version of the LFPQ (Finlayson et al., 2007) was translated by one of

165 the authors, a Brazilian-Portuguese native speaker with English Skills, who translated the English

166 version into Brazilian-Portuguese. The original English task was also sent to a bilingual professor

167 who has expertise in the area and to a certified translator. The versions were compared,

168 discrepancies were discussed, and modifications were made to achieve the most accurate

169 Portuguese version of the task, which was piloted before the experiment.

170

171 Implicit wanting and food preference 172 173 Implicit wanting and food preference were measured using a forced choice

174 methodology. Participants are presented with 96 randomized food pairs ensuring every image

175 from each of the four categories is compared to every image from other categories, and are

176

177 ‘ s are covertly recorded and used to compute mean response times for

178 each food category. Both selection (positively contributing) and non-selection (negatively

179 contributing) are recorded to calculate implicit wanting scores (frequency-weighted algorithm).

180 A positive score indicates a more rapid preference for a given food category relative to the

181 alternatives in the task and a negative score indicates the opposite. A score of zero would

182 indicate that the category is equally preferred (Figure 2).

183

184 Figure 2 Representation of the paired foods instructions and the implicit wanting trials of the 185 LFPQ-BR.

186 *Which food do you most want to eat now?

187 188 Explicit Liking and Explicit Wanting 189 190 To measure explicit liking and explicit wanting, participants were presented with food

191 images individually and were

192 H

193 H

194 for the explicit wanting measure. Final scores range from 0 to 100 (Figure 3).

195

196 Figure 3 Representation of the single foods instructions, explicit liking (a) and explicit wanting 197 (b) trials of the LFPQ-BR.

198 (a)How pleasant would it be to taste some of this food now? 199 (b)How much do you want some of this food now? 200 * Not at all ------Extremely 201

202 Fat Appeal Bias and Taste Appeal Bias

203

204 For each one of the constructs of food reward (explicit liking and wanting; and implicit

205 wanting) the fat appeal bias and sweet appeal bias were calculated for explicit liking, explicit

206 wanting or implicit wanting, according to high fat relative to low fat foods (subtracting the mean 207 high fat values from the mean low fat values), and sweet relative to savoury foods (subtracting

208 the mean sweet values form the mean savoury values).

209 c) Experimental procedure using the LFPQ-BR 210 211 Participants and Study Design 212 213 Participants were recruited by social media and flyers with a call to participate in

214 research about food preferences and behaviour. Forty-eight adult (above 18 and under 60 years)

215 male and female participants were included. They participated in a voluntary basis, as they did

216 not receive any form of reward for the study. Exclusion criteria were smoking, pregnancy,

217 diagnosed metabolic disease such as diabetes and hypo - or hyperthyroidism, and allergies

218 and/or aversion to the food served in the test meal or included in the LFPQ-BR.

219 The experiment was set up at the Dietary Technique Laboratory, which has a private air-

220 conditioned room where the participants were evaluated. Screening was performed by email to

221 investigate inclusion and exclusion criteria, and participants were asked to attend the laboratory

222 following a 4-hour fast on a previously scheduled appointment between 11am and 13pm.

223 Informed consent was obtained, and height and weight were measured with participants

224 standing, wearing light clothes and no shoes. This was followed by testing the food preferences

225 of the participants in fasted state, evaluated by the LFPQ-BR. A standardised test meal was

226 provided, then 10 minutes after consuming it, the LFPQ-BR was undertaken again to evaluate

227 food preferences in the fed state. Subjective appetite measures were undertaken before and

228 after the test meal. Finally, the Binge Eating Scale was completed at the end of the experiment.

229 The whole visit for each participant lasted approximately 1 hour. This study was formally

230 approved by the ethical committee of the Federal University of São Paulo (#0531/2016) and was

231 carried out in accordance with the principles of the Declaration of Helsinki.

232 Test Meal 233 The test meal was fixed at the same amount for all participants and was piloted before

234 the experiment to adjust for taste acceptance and suitability of the amount provided. The lunch

235 consisted of 500 gram ( 650 kcal) of penne, meatballs and mixed buttered vegetable (carrots,

236 barred potato, broccoli and fine beans), 150 ml of orange juice and 150 ml of water. The test

237 meal was planned to be predominantly savoury and we aimed to plan a balanced meal in terms

238 of macronutrient distribution and calorie amount (Ministério do Trabalho e Emprego, 2006;

239 Ministry of Health of Brazil, 2014) (Table 1). It was prepared fresh every day and participants

240 were instructed to consume the meal in its entirety.

Table 1 Nutritional value of the test meal planned to be balanced in terms241 of macronutrient distribution and calorie amount. Food Total (g) Protein (g) Fat (g) Carbohydrates g) Pasta 120.5 4.08 0.48 27.60 Tomate sauce 110 1.54 10.01 7.40 Meat Balls 124 26.59 11.13 11.71 Buttered vegetebles 149 2.28 3.30 14.37 Orange juice 150 1.05 0.15 11.40 Total 653.5 35.54 25.07 72.48 242

243 Binge Eating Scale (BES) 244 245 Binge eating symptoms were assessed using the Binge Eating Scale (BES). This is a 16-

246 item self-report instrument to investigate behavioural manifestations (eight items) and feelings

247 and cognitions (eight items) of binge eating. For each item, three or four statements are given

248 that increase in severity and the participant is asked to select a statement that best describes

249 how they usually behave/feel. Scores are summed and binge eating behaviour is classified into

250 levels of severity: mild (scoring 17 or less), moderate (18- (Freitas et al.,

251 2001; Gormally et al., 1982).

252

253 Appetite Measures

254 255 In order to verify the efficacy of the manipulation of the hunger/satiation state,

256 subjective appetite measures were undertaken before and after the test meal, using a paper-

257 based visual analog scale (VAS), where the participant selected a position on a continuous

258 100mm linear scale to represent their answer to the question. To evaluate hunger the

259 participant answered the question

260 D

261 food could you eat? F

262 evaluate fullness t were

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265 Statistical analysis

266 All variables underwent a compliance test to check their distribution against theoretical

267 curves. For this, histograms were analyzed and the Kolmogorov-Smirnov test for normality and

268 Levene test were applied to verify homoscedasticity.

269 General Linear Models (GLM) were used to observe the main effects and its interactions

270 of taste (savory or sweet), fat (high or low) and condition (fasted or fed) among dependent

271 variables: explicit liking, explicit wanting and implicit wanting. The t-Student test for related

272 samples was used to compare different scores between two dependent groups.

273 Multiple linear regression models were developed to investigate the relationship

274 between independent variables and dependent variables: explicit wanting of high fat sweet

275 food, explicit liking high fat sweet food, implicit wanting high fat sweet food, explicit wanting

276 sweet appeal bias and implicit wanting fat appeal bias. It was tested as independent variables

277 for P dependent variables and

278 only those with the statistically significant coefficient of regression remained. The insertion of 279 variables in the models was done by stepwise method with forward selection. The fit of the

280 models was evaluated by residual analysis.

281 All analyses were conducted using Statistical Package for Social Sciences (SPSS) for

282 Windows version 15.0.1. For all analyses, results were considered significant with p<0.05.

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299 300 RESULTS

301 a) Food database validation

302 Results from the validation demonstrated that 33 food images were properly recognized

303 and habitually consumed. Foods were considered adequate if their mean values on the 7-point

304 Likert scale answers were above 5 for the high content of fat, calorie and sweet taste; and lower

305 than 3 for the low content of fat, calorie and sweet taste. (Table 2). Further, we conducted

306 cluster analysis to confirm the classification of each food in the category they were assigned.

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319 Table 2 Thirty three food cues validated for the LFPQ-BR, divided into the four categories.

Is it high Have Is it fat? Is it sweet? Food Reconize? Like? calorie? eaten? (mean ± SD) (mean ± SD) (mean ± SD) Chocolate M&Ms 100% 100% 90.2% 5.61 ± 0.8 5.94 ± 0.7 5.84 ± 0.7 Chocolate cheesecake 91.9% 90.2% 80.5% 5.45 ± 0.7 5.51 ±0.7 5.78 ± 0.8 Ice cream 100% 99.2% 90.2% 6.16 ± 0.8 6.00 ± 0.8 6.23 ± 0.8 Milk chocolate 99.2% 100% 91.1% 5.39 ± 0.8 5.54 ± 0.8 5.61 ± 0.8 Paçoca1 88.9% 92% 80.5% 5.30 ± 0.9 5.75 ± 0.9 5.67 ± 0.9

High Fat Sweet Sweet HighFat Chocolate chip cookies 99.2% 99.2% 80.5% 5.40 ± 0.8 5.52 ± 0.8 5.62 ± 0.9 Chocolate Balls* 99.2% 92% 80.5% 5.46 ± 0.9 5.54 ± 1.1 5.85 ± 0.8 Red Grapes 100% 100% 88% 1.41 ± 0.7 5.08 ± 0.7 2.46 ± 1.1 Fruit Salad 98.1% 97% 82.2% 1.80 ± 0.8 5.08 ± 0.7 2.73 ± 0.9 Watermelon 100% 98.1% 83.3% 1.00 ± 0.0 5.12 ± 0.7 2.22 ± 1.1 Papaya 100% 97% 82.2% 1.69 ± 0.8 5.08 ± 0.7 2.36 0.9 Melon 97% 97% 85.8% 1.41 ± 0.7 5.05 ± 0.7 1.99 ± 0.9

Low Fat Sweet Fat Low Banana 100% 99.2% 89.4% 1.58 ± 0.8 5.13 ± 0.7 2.77 ± 1.0 Mango* 92% 92% 85.8% 2.39 ± 1.2 5.20 ± 1.0 2.85 ± 1.1 Cheeseburger 100% 98.4% 80.5% 5.93 ± 0.8 1.40 ± 0.7 6.18 ± 0.7 Pepperoni pizza 99.2% 99.2% 90.2% 5.89 ± 0.9 1.77 1.0 6.15 ± 0.7 Coxinha2 99.4% 97.5% 85.8% 6.30 ± 0.7 1.64 ± 0.9 6.42 ± 0.6 Pastel frito3 98.1% 96.3% 83.3% 5.88 ± 0.7 1.77 ± 0.9 5.86 ± 0.7 Pão de queijo4 98.8% 98.1% 91.3% 5.08 ± 0.7 1.72 ± 0.9 5.09 ± 0.9 French Fries 98.4% 97.6% 86.2% 5.89 ± 0.8 1.26 0.5 5.98 ± 0.8

High Fat Savoury Savoury HighFat Croissant with ham and 100% 95.9% 86.2% 5.29 ± 1.1 1.93 ± 1.1 5.65 ± 1.0 cheese* Salami slices* 100% 96.7% 91.3% 5.98 ± 0.9 1.51 ± 1.1 5.86 ± 1.1 Peanuts* 95.6% 95.6% 85.1% 5.08 ± 1.1 2.07 ± 1.4 5.17 ± 1.2 Brocoli 100% 100% 82.1% 1.00 ± 0.0 1.00 ± 0.0 1.23 ± 0.5 Letuce-Tomato salad 100% 97.5% 95.1% 1.00 ± 0.0 1.35 ± 0.6 1.00 ± 0.0 Sweetcorn on the cob 100% 99.4% 87.7% 2.28 ± 1.0 1.91 ± 0.9 2.59 ± 0.9 Chicken-salad sandwich 97.5% 92% 80.2% 2.29 ± 0.7 1.69 ± 0.8 2.29 ± 0.9 Fine Beans 95.1% 90.2% 83.3% 1.64 ± 0.8 1.43 ± 0.8 1.89 ± 0.8 Tomatoes 100% 99.2% 83% 1.00 ± 0.0 2.03 ± 0.9 1.62 ± 0.8 Carrots* 100% 100% 85.8% 1.33 ± 0.6 2.37 ± 1.1 1.85 ± 1.0 Low Fat Savoury Fat Low Cod Meal* 99.2% 96.7% 82.9% 2.43 ± 1.1 2.43 ± 1.0 2.43 ± 1.1 Mixed salad* 100% 99.2% 80.5% 1.27 ± 0.6 1.35 ± 0.6 1.58 ± 0.8 Taboulli* 85.8% 82.2% 80.5% 2.38 ± 1.0 1.85 ± 1.1 2.85 ± 1.0 320 1 Candy made with peanut; 2 - Shredded chicken meat, covered in dough, molded into a shape resembling a chicken leg, battered and fried; 3 - half-circle or rectangle-shaped thin crust pies with assorted fillings, 321 fried in vegetable oil; 4 - a type of baked starch tart cookie, salt vegetable oil and cheese. SD = standard deviation. *Food image validated but not used in the present experiment. 322 Table 3 presents the results of the hierarchical cluster analysis for three variables

323 (sweetness, content of fat, and calories). For each variable, three clusters were generated (C1,

324 C2 and C3). 325 Table 3 Results of hierarchical cluster analysis for three variables (content of fat, content of sweetness and calorie amount). Mean Intraclass distance Suggested Variable Cluster Clustered food items variation of cluster name centroid French Fries, Cheeseburger, Pastel Frito, Pepperoni pizza, Coxinha, Pão de queijo, Chocolate cheesecake, Milk chocolate, Chocolate M&Ms, Paçoca, Ice cream, C1 94.96 9.37 High fat Chocolate chip cookies, Croissant with ham and cheese, Chocolate balls, Oreos, Cheese puffs, Glazed doughnut, Salami slices, Qindim, Peanuts. Chicken-salad sandwich, Letuce-Tomato salad, Tomatoes, Sweetcorn on the cob, Fine Beans, Brocoli, Content of Watermelon, Banana, Red Grapes, Papaya, Melon, C2 103.48 9.32 Low fat fat Fruit Salad, Carrots, Cod meal, Cucumber, Mixed salad, Tabouli, Mango, Apple, Strawberries, Orange, White rice. Cake, Biscuit cereal bar, Cream Crackers, Yogurt with strawberry and blueberry, White toast with butter, Bread roll, Caramel cereal bar, Cheese & crackers, Probably high C3 196.81 13.44 Beef sandwich, Muffin, Cashews, Scrambled eggs and fat toast, White toast with jam, Light yogurt, Brie, Marshmallow, Biscoito de polvilho, Boiled sweets. French Fries, Cheeseburger, Pastel Frito, Pepperoni pizza, Coxinha, Pão de queijo, Chicken-salad sandwich, Letuce-Tomato salad, Tomatoes, Sweetcorn on the cob, Fine Beans, Brocoli, Carrots, Croissant with ham C1 121.57 10.64 and cheese, Cod meal, Cucumber, Cheese puffs, Low sweet Sweetness Salami slices, Mixed salad, Peanuts, Tabouli, Cream Crackers, White toast with butter, Cheese & crackers, Beef sandwich, Cashews, Scrambled eggs and toast, Brie, Biscoito de polvilho, White rice. Chocolate cheesecake, Milk chocolate, Chocolate C2 104.63 9.84 High sweet M&Ms, Paçoca, Ice cream, Chocolate chip, cookies, Watermelon, Banana, Red Grapes, Papaya, Melon, Fruit Salad, Chocolate balls, Oreos, Glazed doughnut, Cake, Quindim, Mango, Caramel cereal bar, Muffin, Marshmallow, Boiled sweets. Biscuit cereal bar, Yogurt with strawberry and Probably high C3 170.35 11.99 blueberry, Apple, Bread roll, White toast with jam, Strawberries, Orange, Light yogurt. sweet French Fries, Cheeseburger, Pastel Frito, Pepperoni pizza, Coxinha, Pão de queijo, Chocolate cheesecake, Milk chocolate, Chocolate M&Ms, Paçoca, Ice cream, C1 102.73 9.70 Chocolate chip cookies, Croissant with ham and High calorie cheese, Chocolate balls, Oreos, Cheese puffs, Glazed doughnut, Salami slices, Cake, Quindim, Peanuts, Bread roll, Muffin, Marshmallow, Boiled sweets. Chicken-salad sandwich, Letuce-Tomato salad, Content of Tomatoes, Sweetcorn on the cob, Fine Beans, Brocoli, Calories C2 130.03 9.10 Watermelon, Banana, Red Grapes, Papaya, Melon, Low calorie Fruit Salad, Carrots, Cod meal, Cucumber, Mixed salad, Tabouli, Mango, Apple, Strawberries, Orange. Biscuit cereal bar, Cream Crackers, Yogurt with strawberry and blueberry, White toast with butter, Caramel cereal bar, Cheese & crackers, Beef sandwich, Probably high C3 176.35 12.68 Cashews, Scrambled eggs and toast, White toast with calorie jam, Light yogurt, Brie, Biscoito de polvilho, White rice.

326 327 The foods allocated on the C3 clusters of each analysis were not considered validated

328 because the perception of the total fat, sweetness and/or calorie content appeared confusing

329 to respondents. In other words, the third cluster indicated the participants did not consistently

330 perceive that food as high or low fat/sweet/calorie.

331 Considering the above, a set of 33 food images were validated (Supplementary Figure 1) and

332 24 food images were used in the present experiment, 4 for each category and 2 backups for each

333 category. The set of 16 foods were shown to participants before they started the task for the

334 first time and the backup images were used when the participant reported strong disliking for

335 any of the foods originally presented. The results of the food not validated for the LFPQ-BR can

336 be seen in Supplementary Table 1 and the food pictures not validated are shown in

337 Supplementary Figure 2.

338 b) LFPQ-BR experimental results

339 Twenty-one males and 27 females, born in Brazil, with a mean BMI 26.6 (±0.9) kg/m2,

340 ranging from 16.46 kg/m2 to 54.28 kg/m2 were evaluated in this study. Mean age was 32.8 (±1.4)

341 years and they presented mean weight of 75.1 (±2.9) kg and height 1.67 (±0.01) metres. Family

342 income was distributed as follow: 10.4% earn up to 2 minimum wages; 25% 2-4 minimum wages;

343 31% 5-10 minimum wages; 20.8% more than 10 minimum wages; and 12.8 % did not respond

344 to this question. Percent of educational attainment levels was: 2.1% lower secondary level; 4.2%

345 upper secondary level; 18.8% incomplete tertiary level; 20.8% complete tertiary level; and 54.1%

346 post graduate level.

347 The results are expressed as mean (standard error). Fasted and fed scores of explicit liking,

348 implicit wanting and explicit wanting of high fat savoury, low fat savoury, high fat sweet and low

349 fat sweet, fat appeal bias and taste appeal bias are shown in Table 4.

350 Table 4. Implicit wanting, explicit liking and explicit wanting for the food categories and appeal biases of the LFPQ- BR on fasted and fed states. Fasted Fed Explicit Implicit Explicit Implicit Explicit Explicit Liking Liking Wanting Wanting Wanting Wanting £ £ £ HFSA 66.8 (3.6) 16.3 (5.2) 60.9 (4.0) 9.5 (1.8) -47.8 (2.0) 8.8 (1.8) £ £ £ LFSA 65.6 (2.8) 15.1 (3.7) 66.4 (2.8) 13.6 (2.0) -26.2 (2.9) 12.2 (2.1) £ HFSW 46.3 (3.4) -24.0 (4.7) 41.3 (3.3) 57.4 (4.1) 38.7 (3.6) 53.2 (4.1) £ LFSW 51.6 (3.3) -7.5 (4.8) 50.4 (5.5) 52.3 (4.6) 35.2 (3.1) 49.1 (4.6) Fat Appeal Bias -2.0 (3.5) -7.6 (6.19) -7.3 (3.7) 0.5 (2.0) -9.0 (4.2) 0.3 (2.2) Sweet Appeal Bias -17.2 (3.5) -31.5 (5.7) -17.8 (3.7) 43.3 (3.8) 74.0 (2.8) 40.7 (3.7) HFSA = High Fat Savoury; LFSA = Low Fat Savoury; HFSW = High Fat Sweet; LFSW = Low Fat Sweet

Two way interaction between condition and taste; p<0.001 £ Three way interaction between condition, taste and fat; p<0.001 351 352 Food reward in fasted and fed states

353 Explicit Liking

354 Explicit liking was greater in fasted compared to fed state (p<0.001) and there was a

355 greater explicit liking in general for sweet foods (p<0.001). As shown in Table 4, there was an

356 interaction between condition and taste with a greater liking for sweet foods on fed compared

357 to fasted condition and a three way interaction between condition, taste, and fat with explicit

358 liking for high fat and low fat savoury foods decreasing in the fed compared to the fasted state.

359 Implicit wanting 360 361 There was a main effect of taste with implicit wanting being higher for sweet compared

362 to savoury food (p<0.001). There was an interaction between condition and taste as there was

363 an increase in implicit wanting for sweet foods in the fed compared to fasted state. Finally, there

364 was an interaction between condition, taste and fat: implicit wanting for high fat and low fat

365 savoury were higher in the fasted compared to fed state and the opposite for high fat and low

366 sweet (Table 4).

367

368 Explicit Wanting

369 Explicit wanting was higher in the fasted state (p<0.001) and a greater for sweet

370 compared to savoury foods (p<0.001). An interaction between condition and taste showed a

371 greater explicit wanting for sweet foods in the fed compared to fasted state and, lastly, there

372 was a three way interaction between condition, taste and fat with explicit wanting for high fat

373 savoury and low fat savoury foods being higher in the fasted compared to fed state (Table 4).

374

375

376 377 Comparisons between high and low binge eating scorers, sex and food reward in fasted and

378 fed states

379 Univariate analysis showed that the explicit liking sweet appeal bias (explicit liking for

380 sweet relative to savoury food) was greater in individuals who scored more (scored greater than

381 7.5 [median value]) on binge eating symptoms (p=0.03). Additionally, individuals with higher

382 binge eating symptoms scores presented greater explicit wanting for high fat sweet in the fed

383 state (p=0.04), while scoring lower on explicit wanting low fat sweet, although this last result

384 only approached significance (p=0.09).

385 Comparisons between sexes showed that women had greater implicit wanting for high

386 fat sweet (-12.9 versus -38.3; p=0.007) and implicit wanting sweet appeal bias (-21.2 versus -

387 44.9; p=0.03) compared to men in the fasted state. In the fed condition, implicit wanting high

388 fat sweet was also greater for women (45.3 versus 30.3, p=0.03).

389

390 Independent predictors of food reward 391 392 All measures of the LFPQ (36 measures) in fasted and fed states were tested for

393 correlation with the BMI. Six of these measures presented r>0.25 and p<0.05. A

394 positive association between BMI and implicit wanting fat appeal bias in the fasted state

395 (r=0.329; p=0.023) and with implicit wanting high fat sweet in the fasted state (r=0.411; p=0.004)

396 was observed. A negative correlation was also found between BMI and implicit wanting low fat

397 savoury (r=-293; p=0.043) and explicit wanting low fat savoury (r=-0.331; p=0.021) in fasted

398 state. In the fed condition explicit wanting low fat sweet (r=-0.287; p=0.048) and explicit liking

399 low fat sweet (r=-0.301; p=0.038) presented a negative correlation with BMI.

400 As can be seen in Table 5, regression analysis showed that BMI was an independent

401 factor of implicit wanting high fat sweet in the fasted state, and sex (female) was an independent

402 factor of this variable in fasted and fed states. BMI was also an independent predictor of fat 403 appeal bias in the fasted state after the result was adjusted for sex. In addition, explicit wanting

404 high fat sweet and sweet appeal bias was associated with binge eating symptoms and age in the

405 fed state, but not when fasted. Lastly, binge eating symptoms were also an independent

406 predictor of explicit liking high fat sweet in the fed state and not the fasted state.

407 It was not possible to fit a significant model for the variables high fat savoury, low fat

408 savoury and low fat sweet in fasted and fed states.

409 410 Table 5 Multiple regression analysis of LFPQ measures and independent factors in fasted and fed states. Fasted Fed Model Independent Standardized P Independent Standardized p Variables coefficients Variables coefficients () () Explicit Constant† 34.47 0.001 Constant 90.50 <0.001 Wanting Age (years) -0.13 0.36 Age (years) -0.30 0.03 HFSW BES 0.21 0.15 BES 0.38 0.008 BMI (kg/m²)* -0.24 0.11 Explicit Liking Constant† 52.16 <0.001 Constant 92.60 <0.001 HFSW Age (years) -0.17 0.24 Age (years)* -0.25 0.09 BES 0.06 0.66 BES 0.32 0.02 Implicit Constant -67.64 <0.001 Constant 45.31 <0.001 Wanting Sex†† -0.40 0.001 Sex†† -0.30 0.03 HFSW (1=F;0=M) (1=F;0=M) BMI (kg/m²) 0.42 0.002 Explicit Constant† -38.07 0.02 Constant 56.04 <0.001 Wanting Sweet Age (years) 0.21 0.15 Age (years) -0.35 0.01 Appeal Bias BES 0.06 0.65 BES 0.32 0.02 Implicit Constant -57.99 0.02 Constant† -14.84 0.39 Wanting Fat BMI (kg/m²) 0.33 0.02 BMI (kg/m²) 0.11 0.43 Appeal Bias Sex†† -0.18 0.19 Sex††* -0.27 0.06

411 HFSW = High Fat Sweet 412 Bold values are significant variables (p<0.05) of significant models; *Adjustment variable; † not significant model; ††F= female; M=male; BMI=Body Mass Index; BES= 413 Binge Eating Scale 414 Subjective appetite measures in fasted and fed conditions

415

416 Results from the VAS indicated a good manipulation of fasted/fed state. Subjective

417 measures of hunger (62.8 ± 21.1 vs 4.0 ± 7.1; p<0.001) and desire to eat (61.2 ± 18.0 vs 7.2 ±

418 14.6; p<0.001) decreased significantly, while fullness (27.3 ± 21.3 vs 87.6 ± 11.4) increased

419 significantly after consuming the test-meal. A negative correlation was found between fullness

420 and binge eating scores in the fed condition (r=-0.391; p<0.001).

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434

435 436 DISCUSSION

437 The main aim of this study was to test the sensitivity of a culturally adapted version of

438 the Leeds Food Preference Questionnaire for a Brazilian population (LFPQ-BR). The LFPQ

439 presents a standardised methodology to measure distinct psychological constructs of food

440 reward (Liking and Wanting), which is novel in Brazil. The results showed there was consistency

441 between the LFPQ-BR results and previous studies in the literature on food reward with

442 decreased explicit liking, implicit wanting and explicit wanting for food in general in the fed state,

443 and an increase for sweet preferences after a savoury test meal. Binge eating symptoms were

444 confirmed to be a relevant predictor for high fat sweet liking and wanting in the present

445 population. Importantly, we evaluated the procedure using male and female participants with a

446 wide range of BMI, which enabled us to test for the effect of adiposity and sex on LFPQ outcomes

447 for the first time.

448 It was observed that some foods were not properly (or consistently) recognized in terms

449 of their nutritional value (sweetness and content of fat and calories). Therefore, some food

450 images were not considered validated for the LFPQ-BR. Additionally, a few foods were not

451 confirmed as being habitually consumed and/or liked, thus were not considered adequate for

452 use even though they were adequately perceived in terms of their nutritional value. All together,

453 these measures were taken to ensure the instrument is culturally adapted.

454 Our results are consistent with those reported in previous studies using the LFPQ with

455 increased ratings of explicit measures of liking and wanting for food in general under fasted

456 compared to fed conditions (Alkahtni et al., 2016; Cameron et al., 2014; Finlayson et al., 2008),

457 Furthermore, we were able to observe that explicit liking, explicit wanting and implicit

458 wanting for both high and low fat savoury foods decreased in the fed compared to fasted state.

459 This three way interaction is a result consistent with Griffioen-Roose et al. (2010) who reported

460 a decrease of liking and wanting of snacks with a similar taste from a given preload, being that 461 savoury would have a stronger modulating effect on subsequent food choice than a sweet

462 preload. Thus, we were able to observe in our study a form of sensory specific satiation after a

463 predominantly savoury test-meal.

464 We found a two-way interaction between condition and taste on the three components

465 of food reward: explicit liking, explicit wanting and implicit wanting for sweet foods was

466 increased to a greater extent under fed compared to fasted states. In other words, in addition

467 to sensory specific satiation, we also observed an increased implicit and explicit wanting and

468 explicit liking of sweet taste under fed compared to fasted states after a savoury meal, which

469 also indicates a separation in liking and wanting in a manner consistent with the previously

470 consumed food (transition from a wanting and liking of a savoury to a sweet taste after a savoury

471 meal)Therefore, the cited results support the sensitivity of the LFPQ-BR to identify a switch in

472 taste preference after a test meal.

473 Regression analyses highlighted that BMI was an independent predictor of implicit

474 wanting fat appeal bias after adjusting for sex, meaning that BMI would similarly predict greater

475 implicit wanting for high fat relative to low fat food in men and women. This finding is

476 conceptually interesting as the implicit wanting ratings are measured by a behavioural forced

477 choice methodology when the participant is instructed to select the food presented in pairs

478 Thus, the

479 motivated behavioural response may operate independently from the explicit awareness of a

480 food perceived hedonic value (Finlayson et al., 2008). As further support to present the LFPQ-

481 BR as a valid method, this finding is in accordance to Nijs et al (2010) who reported a tendency

482 in overweight/obese women especially in a hungry state to have an enhanced automatic

483 orientation towards food pictures compared to lean women. In fact, evidence for an association

484 between reward sensitivity and BMI has been shown, suggesting that genotype, dietary factors,

485 and signals from adipose tissue that are altered by weight status may have an effect on 486 dopaminergic transmission (Horstmann, 2017). Therefore, the results presented here implying

487 an effect of weight status on high fat food preference, highlights the sensitivity of this simple,

488 easy-to-use and accurate behavioural task (LFPQ) to provide important evidence in human food-

489 reward research.

490 When it comes to sweet preferences, an interesting sex and BMI effect was found in our

491 regression model. It was observed that sex (with female being the indicative category) and BMI

492 were independent factors of implicit wanting high fat sweet foods in the fasted state and sex

493 remained as a predictor in the fed state. Previous studies have shown that females tends to

494 prefer sweet-related comfort food, while males tends to prefer savoury meal-comfort food

495 Wansink et al (2003) and impaired control over eating sweets and mood altering effects of eating

496 sweets were found to be more likely in female than male participants (Kampov-Polevoy et al.,

497 2006). Why or how the hedonic sensitivity to this type of food is sex-dependent is largely

498 unknown, however, animal data offer some support, demonstrating sex-related effects on gene

499 expression in the mesolimbic reward system after a high fat and high-sugar cafeteria diet Ong

500 et al (2013).

501 We also sought to verify the role of binge eating symptoms on the LFPQ-BR results.

502 Previously, (Dalton et al., 2013a) have shown that both lean and obese women with high scores

503 on the BES had enhanced wanting for high fat sweet foods and increased intake and/or craving

504 for this type of food. We found that individuals with higher scores of binge eating symptoms

505 presented greater explicit wanting for high fat sweet food in the fed state. Additionally, we

506 observed greater explicit liking sweet appeal bias (explicit liking for sweet relative to savoury

507 food) in the higher BES group compared to the lower BES group. Kampov-Polevoy et al. (2006)

508 suggested that craving and impaired control over eating sweets is related to sweet liking. Earlier,

509 Greeno et al. (2000) also linked the hedonic response to sweet taste to binge eating. 510 Moreover, the BES score was an independent predictor of explicit wanting and liking for

511 high fat sweet food and explicit wanting sweet appeal bias. A greater wanting and liking (hedonic

512 hunger) for high fat sweet foods in the absence of hunger (fed condition) on individuals who

513 scored higher on BES was observed. In fact, highly palatable food continues to be consumed

514 even when energetic needs are satisfied, which does not happen to the same extent with

515 standard food (Finlayson et al., 2007; Kenny, 2011) and this outcome for binge eating

516 strengthens the validation of the LFPQ-BR. Previously, Nasser et al (2008) have demonstrated

517 that obese individuals with binge eating showed increased motivation for food when satiated,

518 but not when hungry. Therefore, we suggestthat in the presence of physiological hunger the

519 results would be more balanced between higher binge eating and lower binge eating groups and

520 the difference would become greater when hunger was suppressed.

521 Although the manipulation of hunger state was efficient (evaluated using a 100mm

522 visual analog scale before and after the test meal) we observed a negative association between

523 fullness and binge eating scores at fed condition and a positive association between binge eating

524 scores and hunger also after the meal, but this last result only approached significance. It is

525 important to mention that reward-driven eating has been suggested to override the effect of

526 satiety (Berthoud and Morrison, 2008), however, this is still a provocative idea because it has

527 also been shown that weakened satiety and elevated post prandial hunger are features of binge

528 eating disorders (Sysko et al., 2007). Therefore, in accordance with Finlayson et al. (2011), the

529 results presented in this study would indicate that trait binge eating would be related to

530 differences in both hunger and reward. Future studies are needed to confirm this hypothesis as

531 (a) we have not evaluated a binge eating clinical sample and (b) we did not have high levels of

532 binge eating severity in our sample. Thus, these findings should be interpreted with caution.

533 This study has some strengths and limitations. As cited, we did not evaluate a binge

534 eating clinical sample, which could have given more clear results. Nevertheless, we were able to 535 distinguish responses related to hedonic eating in individuals who scored higher or lower on the

536 binge eating scale. On the other hand, we used a wide range of BMI and this could be taken as

537 a strength of the present study, along with the effort of having a measure of hunger/satiation

538 to test the state manipulation. Another limitation was the sample size. Because our study is

539 complex and employed a number of steps, it was hard to recruit participants. Some non-

540 significante effects and differences observed in the univariate analysis could be significant in a

541 larger sample. Furthermore, investigating a more heterogeneous sample in terms of

542 socioeconomic status would be a very interesting issue for future research.

543 In conclusion, the LFPQ-BR, evaluated before and after a fixed test meal, demonstrated

544 good consistency with previously reported outcomes using the original version of the platform.

545 We were able to distinguish responses according to adiposity and perturbed eating behavior and

546 demonstrated important sex-dependent food choices. Therefore, the results presented here

547 indicate that the LFPQ-BR is a potentially useful instrument for the evaluation of liking and

548 wanting for food in the Brazilian population.

549

550 FUNDING: This research was supported by FAPESP (Fundação de Amparo à Pesquisa do Estado

551 de São Paulo), grant #2016/17230-7.

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